> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Late Shipments, Deutsche Post > Late Shipments for Deutsche Post stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Key Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Delivery Performance](/nerve-centre/connectors#connectors-by-type) ## At a glance > Absolute count of Deutsche Post / DHL Paket consignments that arrived at the addressee after their service-code transit aim in the trailing 7 days. This is the numerator behind the [On-Time Delivery Rate](/nerve-centre/kpi-cards/deutsche-post/on-time-delivery-rate) percentage, expressed as a raw workload figure. Where the OTD dial tells the operations lead how the network is performing, this count tells the customer-service team how many "wo ist meine Bestellung" (where is my order) contacts are already in flight or about to land. | | | | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **What it counts** | `COUNT(shipments WHERE delivered_at > aim_delivery_date)` over the rolling 7-day window. Each consignment scores once against the transit aim for its own DHL product (DHL Paket = 1 to 2 working days domestic; DHL Päckchen = 2 to 4; EU cross-border = product-specific). Grounded in `detail`: Late Shipments for the selected period. | | **Data source** | DHL Tracking API `GET /track/shipments` event stream, joined to the booked product and label-time despatch date. The card compares the final `delivered` event timestamp against the aim derived from the DHL product code and the German working-day calendar. | | **Service-tier scope** | **Trackable DHL Paket and Päckchen with a delivered scan.** Letter-class products (Brief, Warenpost where untracked) are excluded because there is no delivered scan to compare against the aim. | | **Working-day calendar** | Aim dates respect German public holidays and regional Feiertage (which vary by Bundesland). A parcel booked the day before a Bavarian holiday is not counted late for the holiday day. | | **Packstation / Filiale** | A parcel deposited to a Packstation locker or handed to a Postfiliale within aim counts as **on time**; the recipient's collection delay does not make it late. | | **Return-leg inclusion** | **Outbound only.** Retouren (return) consignments are filtered via `direction = inbound`; return-leg volume is on [Returned to Sender](/nerve-centre/kpi-cards/deutsche-post/returned-to-sender). | | **In-flight handling** | Consignments in transit and still within aim are excluded. Consignments past aim with no delivered scan are counted as late (conservative for the merchant: a never-scanned parcel that has blown its aim is treated as late). | | **Time window** | `7D` (rolling 7 days). Daily readings are noisy below 200 consignments per day. | | **Alert trigger** | `>5% of total` (late count exceeds 5 percent of trackable consignments in the period). For a 4,000-parcel-week merchant that is 200+ late deliveries per week. | | **Roles** | owner, operations | ## Calculation Calculated automatically from your Deutsche Post data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading. ## Worked example A German DTC skincare brand based in Cologne, around 3,900 outbound parcels per week, despatching almost entirely on DHL Paket for domestic orders with a thin EU cross-border tail into Austria. Reading taken at 09:00 CET on 14 Apr 26 for the trailing 7 days (07 Apr 26 to 13 Apr 26). DHL leg only: | Product | Consignments (7D) | Late (delivered after aim) | Late % | | ----------------------------- | ----------------- | -------------------------- | -------- | | DHL Paket (Inland) | 3,180 | 121 | 3.8% | | DHL Päckchen (Inland) | 410 | 19 | 4.6% | | DHL Paket EU (DE to AT) | 240 | 27 | 11.3% | | **All trackable (this card)** | **3,830** | **167** | **4.4%** | The card reads **167** late shipments. The alert at `>5% of total` is **not** tripped at the aggregate (4.4 percent), but the EU cross-border lane alone is at 11.3 percent. Five things to notice: 1. **The percentage felt fine on the OTD card; the count is the workload.** 167 late deliveries equals roughly 167 customer-service contacts currently live or about to arrive. At an estimated 8 minutes per WISMO contact, that is around 22 staff-hours of work generated this week by transit delays alone. The CS team needs the number, not the percentage. 2. **The EU lane is the actionable signal.** DE to AT cross-border at 11.3 percent is well above the domestic baseline. The usual cause is customs clearance friction rather than last-mile delay; check [EU Cross-Border Customs Clearance Rate](/nerve-centre/kpi-cards/deutsche-post/exception-rate) symptoms via the [Exception Rate](/nerve-centre/kpi-cards/deutsche-post/exception-rate) breakdown, and confirm CN23 paperwork is generating cleanly at label time. 3. **Compare to the same week last year and to known network events.** German peak (Weihnachtsgeschäft, roughly late November to 23 December) and the Ostern (Easter) week reliably inflate the late count. The 14 Apr 26 reading sits just after Easter; a 10 to 20 percent week-on-week lift around a Feiertag is normal, not a carrier failure. 4. **The 167 count under-states customer-felt impact on untracked letter products.** Any Warenpost or Brief volume is invisible here. If the brand also ships small flat items as letters, real customer-felt lateness is higher than the trackable count. Use this number for operations planning, not customer-experience modelling. 5. **Pair the count with the claim funnel.** A typical 10 to 25 percent of late parcels escalate; 167 late this week predicts roughly 17 to 42 contacts that may become an [Open Claims](/nerve-centre/kpi-cards/deutsche-post/open-claims) entry over the following fortnight. Budget CS capacity accordingly. ## Sibling cards merchants should reference together Late shipment count is the workload metric. Pair it with these to plan response: | Card | Why pair it with Late Shipments | What the combination tells you | | ------------------------------------------------------------------------------------------------ | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | | [On-Time Delivery Rate](/nerve-centre/kpi-cards/deutsche-post/on-time-delivery-rate) | The percentage view of the same metric. | The percentage tells you how the network is performing; the count tells you how much work the CS team has tomorrow. Track both. | | [Exception Rate](/nerve-centre/kpi-cards/deutsche-post/exception-rate) | What fraction of consignments threw a tracking exception. | A rising exception rate predicts a rising late count at 24 to 48 hours. | | [Open Claims](/nerve-centre/kpi-cards/deutsche-post/open-claims) | How many late deliveries escalated into claims. | Typical conversion is 10 to 25 percent: 100 late deliveries produces 10 to 25 claims (Nachforschungsauftrag investigations). | | [Failed Deliveries](/nerve-centre/kpi-cards/deutsche-post/failed-deliveries) | The absent-recipient / re-attempt tail. | Separates "delivered late" from "could not deliver at all", which need different operational responses. | | [First-Attempt Delivery Rate](/nerve-centre/kpi-cards/deutsche-post/first-attempt-delivery-rate) | Whether re-attempts are driving the lateness. | A falling first-attempt rate and a rising late count together point to a home-delivery-success problem in specific postcodes. | | [OTD by Route](/nerve-centre/kpi-cards/deutsche-post/otd-by-route) | Where the late deliveries concentrate. | If the late count is concentrated in 2 or 3 routes the issue is a local depot; if uniform, it is network-wide. | | [Avg Transit (days)](/nerve-centre/kpi-cards/deutsche-post/avg-transit) | The transit-time distribution behind the lateness. | A late count rising while average transit holds steady means a fat tail, not a network-wide slowdown. | ## Reconciling against the source **Where to look in DHL's own tooling:** The closest like-for-like is the **DHL Geschäftskundenportal** (Business Customer Portal) → **Sendungsübersicht / Performance** view, filtered to *Inland, last 7 days, outbound only, status delivered-after-aim*, or the per-parcel event history in the public DHL Sendungsverfolgung (tracking) portal. For account-level performance, the DHL account manager can supply a Laufzeit (transit-time) report that is the official basis for any service conversation. **Why our number may legitimately differ from DHL's report:** | Reason | Direction | Why | | --------------------------------------- | ----------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Timezone (CET / CEST vs UTC)** | Boundary days off | DHL's portal and scan timestamps are in carrier-local time (CET in winter, CEST in summer). The card stores in UTC. For a 7-day window, boundary-day shifts move a handful of consignments between days; the weekly total typically agrees within 1 percent. | | **Tracking-event ingestion lag** | Ours can lag | The DHL Tracking API surfaces scans in batches, with latency that widens during peak. A parcel delivered a few hours ago may not yet show a delivered event in our index; conservative classification means it stays "in transit" until past aim rather than being booked late prematurely. | | **Working-day / Feiertag calendar** | Either | Regional Feiertage vary by Bundesland. If the portal applies a national calendar and the card applies a regional one (or vice versa), a parcel can score differently around a holiday. | | **Product reclassification mid-period** | Either | A despatch tool that re-books a parcel onto a different DHL product mid-flow can split the population in the portal; the card uses the product code recorded at label time. | | **Peak-period sampling** | Either | During Weihnachtsgeschäft the portal's reporting cadence may show partial data for the most recent days; the card always shows confirmed deliveries plus past-aim in-transit. | **Cross-connector reconciliation:** | Card | Expected relationship | Causes of legitimate divergence | | ---------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | --------------------------------------------------------------------------- | | [`shopify.unfulfilled_orders`](/nerve-centre/kpi-cards/shopify/unfulfilled-orders) | Upstream input: orders waiting on a label cannot meet aim if they sit too long. | Webhook delivery failures, manual fulfilment delays, B2B / pre-order flows. | | [`shopify.refund_rate`](/nerve-centre/kpi-cards/shopify/refund-rate) | Downstream impact: late deliveries drive refund and WISMO contacts at 7 to 14 day lag. | Refund rate has many drivers; lateness is one input. | ***
Documentation cross-reference (for agencies running multiple carriers) Late-shipment counts exist with conceptually similar definitions across other carriers. These are not parallel measurements of the same shipments, they are independent operations. * [`royal_mail.rm_late_shipments_count`](/nerve-centre/kpi-cards/royal-mail/late-shipments) (UK carrier peer) * [`postnord.pn_late_shipments_count`](/nerve-centre/kpi-cards/postnord/late-shipments) (Nordic carrier peer)
## Known limitations / merchant FAQs **Why does the count bounce day-by-day even when OTD looks steady?** Volume seasonality. German despatch volume is heaviest Tuesday to Thursday and lighter on Monday and Friday. A steady 96 percent OTD on a heavy Wednesday produces a noticeably higher late count than the same OTD on a quiet Friday. Use the 7-day rolling view for trend, not the daily reading. **My EU cross-border late rate is far higher than domestic. Is DHL failing?** Usually not the last mile. DE to AT and DE to CH parcels clear customs, and a CN23 paperwork problem at label time, or a customs hold, adds days that the card scores as late even though the German leg ran on time. Check the [Exception Rate](/nerve-centre/kpi-cards/deutsche-post/exception-rate) breakdown for customs-hold events before raising it with DHL, and confirm your label flow generates CN23 data cleanly. **What do I do during a network disruption or strike?** German postal and parcel disruption (for example ver.di industrial action) inflates the late count sharply on affected days. The playbook: (1) suppress alerts on this card for the announced disruption dates so CS is not paged for a known event, (2) shift time-sensitive volume to an alternative carrier (DPD, Hermes, GLS) for the affected window, (3) re-baseline thresholds temporarily and reset when service resumes. Do not permanently raise the threshold. **Letter-class vs parcel: why is some volume invisible here?** Untracked letter products (Brief, untracked Warenpost) generate no delivered scan, so they cannot be compared against an aim and are excluded. If a meaningful share of your volume ships as letters, treat this count as a parcel-only workload figure and read it alongside customer-service ticket signals for the letter tail. **Why does Shopify show "shipped" but DHL tracking shows nothing for a parcel I am about to count as late?** Two-stage handover. Shopify marks shipped at label-print (`fulfilment.created`); DHL's first scan is at collection or Filiale handover, typically 2 to 24 hours later. If 48 hours pass with no first scan, the parcel was probably never collected. The card classifies a never-scanned parcel as in-transit until past aim, then late, even if it never actually reached DHL. Check [Failed Deliveries](/nerve-centre/kpi-cards/deutsche-post/failed-deliveries) for the never-scanned pattern. **What is the playbook when the count crosses 5 percent of total?** In order: (1) check the [DHL Sendungsverfolgung](https://www.dhl.de/de/privatkunden/dhl-sendungsverfolgung.html) status and DHL business-portal notices for declared network incidents, (2) check [Exception Rate](/nerve-centre/kpi-cards/deutsche-post/exception-rate) for upstream address-data or customs issues, (3) check [OTD by Route](/nerve-centre/kpi-cards/deutsche-post/otd-by-route) for regional concentration, (4) raise a Laufzeit / performance conversation with the DHL account manager if (1) to (3) do not explain it. **How do I plan for German peak (Weihnachtsgeschäft)?** Expect this count to roughly double from late November to 23 December. DHL publishes letzte Versandtermine (last despatch dates) each year; treat them as a hard cliff in checkout copy. Three actions: (1) tighten checkout deadline messaging, (2) shift budget volume to an alternative carrier for non-time-sensitive items, (3) confirm sufficient Packstation drop-off capacity for the affected postcodes to keep first-attempt success high. **Does a Packstation deposit count as late if the customer collects days later?** No. The aim is met at the point DHL deposits the parcel into the Packstation or hands it to the Filiale. The recipient's collection delay is outside the carrier's control and does not move this count. That is by design and matches DHL's own service definition. *** ### Tracked live in Vortex IQ Nerve Centre *Late Shipments* is one of hundreds of KPI pulses Vortex IQ tracks across Deutsche Post and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.